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--- |
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license: mit |
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base_model: FacebookAI/xlm-roberta-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cnec |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: CNEC1_1_extended_xlm-roberta-large |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: cnec |
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type: cnec |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8551829268292683 |
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- name: Recall |
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type: recall |
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value: 0.8995189738107964 |
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- name: F1 |
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type: f1 |
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value: 0.8767908309455589 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9694414756758897 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# CNEC1_1_extended_xlm-roberta-large |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2115 |
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- Precision: 0.8552 |
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- Recall: 0.8995 |
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- F1: 0.8768 |
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- Accuracy: 0.9694 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.2948 | 1.72 | 500 | 0.1385 | 0.7752 | 0.8589 | 0.8149 | 0.9620 | |
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| 0.1185 | 3.44 | 1000 | 0.1411 | 0.8063 | 0.8808 | 0.8419 | 0.9692 | |
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| 0.0762 | 5.15 | 1500 | 0.1485 | 0.8252 | 0.8781 | 0.8509 | 0.9690 | |
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| 0.054 | 6.87 | 2000 | 0.1586 | 0.8368 | 0.8878 | 0.8615 | 0.9697 | |
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| 0.0357 | 8.59 | 2500 | 0.1774 | 0.8364 | 0.8990 | 0.8666 | 0.9705 | |
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| 0.026 | 10.31 | 3000 | 0.1869 | 0.8540 | 0.8974 | 0.8752 | 0.9700 | |
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| 0.0189 | 12.03 | 3500 | 0.2040 | 0.8555 | 0.8958 | 0.8752 | 0.9698 | |
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| 0.013 | 13.75 | 4000 | 0.2115 | 0.8552 | 0.8995 | 0.8768 | 0.9694 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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